About MNB Research
Enterprise-grade AI, built by the people who used to audit the enterprises.
MNB Research is an AI products and automation company from Faridabad, India. We started in risk advisory for some of the country's largest balance sheets, and we now package that rigour into thirteen AI products and six service practices for businesses of every size.
Our story
From control reviews to always-on agents.
MNB Research began as an advisory practice. Process audits, control reviews and compliance programmes for listed conglomerates, pharma majors, fintech platforms and hospitality groups taught us one thing above all: the businesses that grow safely are the ones whose systems keep working when nobody is watching.
Then the tools changed. Large-language models made it possible to give a twelve-person firm the always-on capability that used to need a shared-services centre. We rebuilt the company around that: done-for-you AI agents that answer customers, capture leads and clear the busywork, with the approval steps and audit trails we would have insisted on for an enterprise client.
Today MNB is thirteen products and six practices. Featured on Shark Tank India and recognised by DPIIT, we have automated parts of 2,000+ businesses, and every new engagement still starts the way an audit does: by measuring where the hours and the leads are actually going before anything is built.
Leadership
Founder
Mridul Nanda
Founder & Chief Executive Officer
Mridul founded MNB Research to make enterprise-grade AI accessible to every business, and leads its product vision and AI strategy. Before MNB he worked in risk advisory at BDO India, where his clients included Paytm and GSK, and that background is why every MNB product ships with controls, logs and a human approval step built in.
An Economics Honours graduate and CA Finalist, an author and a sportsman, he pairs financial discipline with a builder's impatience for results. He is also the named grievance officer for MNB Research and AbroBot, which is a long way of saying the buck stops with him.
How we work
Four rules we do not bend.
Measure before you build
Every engagement opens with a fixed-price assessment that finds where the hours and the leads are going. If automation will not pay back inside a quarter, we say so.
A human on anything irreversible
Agents prepare decisions; named people take them. Money, tax invoices and delivery promises wait for a tap, and every action is logged where the owner can read it.
No hidden incentives
AbroBot takes no partner commission from universities. Our recommendations point where the evidence is, not where the money is, across every product we make.
Plain pricing
Flat monthly fees per business, published on the pricing page before you ever speak to us. No per-seat surprises and no number that changes once you are on a call.
Company
The facts.
Abrobot Technologies Private Limited
1945 P, Sainik Colony, Sector 49
Faridabad, Haryana 121001, India
DPIIT-recognised startup
Featured on Shark Tank India
Brands: MNB Research · AbroBot
Insights
Latest from the research desk.
Twelve questions that tell you whether anyone will actually pay
Most founder interviews fail the same way: the founder pitches, the other person is polite, and nothing is learned. Here is the structure that works.
InsightDesk research or boots on the ground: which does your idea actually need?
Fieldwork multiplies the cost of validation several times over. Here is how to tell when it is indispensable and when it is theatre.
InsightWhat market validation actually costs in India in 2026
Feasibility studies are quoted in ranges so wide they are useless for budgeting. Here are the real bands, and the four things that move the number.
Start where every MNB engagement starts.
A ₹9,999 AI Transformation Assessment tells you what to automate first, what it will cost to run, and when the first result lands.
The market we work in, in numbers.
References: IBEF — Udyam registrations · PIB, Government of India · EY–NASSCOM AI Adoption Index. Figures carry the year they refer to. Where sources disagree we show both rather than picking quietly.